BIPEFT:以预算为导向的代搜索参数,对大型预训练语言模型进行高效微调
Aofei Chang1, Jiaqi Wang1, Han Liu2
1Pennsylvania State University.
概括
本研究介绍了自动参数高效微调 (PEFT) 的预算导向代搜索策略,以提高效率和性能. 通过分离搜索空间和使用预算导向的早期选择,BIPEFT优化了自动PEFT.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 参数高效微调 (PEFT) 对于适应大型语言模型至关重要.
- 手动PEFT设计往往会导致低于最佳的结果.
- 现有的自动PEFT方法在搜索空间复杂性和效率方面扎.
研究的目的:
- 开发一个更高效和有效的自动PEFT策略.
- 为了应对搜索空间纠和参数预算集成方面的挑战.
- 为了提高下游任务的PEFT性能.
主要方法:
- 引入了自动PEFT (BIPEFT) 的预算导向代搜索策略.
- 采用代搜索来解开二进制模块和排名维度搜索空间.
- 根据参数预算指导设计的早期选择策略.
主要成果:
- BIPEFT显著提高了自动PEFT中的搜索效率.
- 早期选择策略通过删除不重要的模块来加速学习.
- 在低参数预算的公共基准上,BIPEFT的表现明显优越.
结论:
- BIPEFT为自动PEFT提供了一种高效和有效的解决方案.
- 预算导向的代方法克服了以前方法的局限性.
- 在下游任务中使用最小参数实现高性能.
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